Velocity tentative PSO: An optimal velocity implementation based particle swarm optimization to solve traveling salesman problem
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| date | 2015-09-03 09:21:09 |
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| internalnotes | [1] R. Eberhart and J. Kennedy, “A New Optimizer Using Particles Swarm Theory,” in Proc. Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, October 1995, pp. 39–43. [2] J. J. Liang, A. K Qin, P. N Suganthan and S Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” Journal of evolutionary computation, vol. 10, no. 3, pp. 281–295, 2006. [3] A Banks, J. Vincent, and C. Anyakoha, “A review of particle swarm optimization. Part II: hybridization, combinatorial, multicriteria and constrained optimization, and indicative applications,” Journal of Natural Computing, vol. 7, no. 1, pp. 109–124, 2007. [4] L. Chuang, Y. Lin, and C. Yang, “Data Clustering Using Chaotic Particle Swarm Optimization,” IAENG International Journal of Computer Science, vol. 39, no. 2, pp. 208–213, 2012. [5] Z. Zhong and D. Pi, “Forecasting Satellite Attitude Volatility Using Support Vector Regression with Particle Swarm,” IAENG International Journal of Computer Science, vol. 41, no. 3, pp. 153– 162, 2014. [6] Hong Zhang, “An Analysis of Multiple Particle Swarm Optimizers with Inertia Weight for Multi-objective Optimization,” IAENG International Journal of Computer Science, vol. 39, no. 2, pp. 190– 199, 2012. [7] Y. F. Liao, D. H. Yau and C. L. Chen, “Evolutionary algorithm to traveling salesman problems,” Computers & Mathematics with Applications, Elsevier Publisher, vol. 64, no. 5, pp. 788–797, 2012. [8] M. R Bonyadi, M. R Azghadi and H. S Hosseini, “PopulationBased Optimization Algorithms for Solving the Travelling Salesman Problem,” Travelling Salesman Problem, Book edited by: Federico Greco, InTech Publisher, Vienna, Austria 2008. [9] X. Yan, C. Zhang, W. Luo, W Li, W, Chen and H. Liu, “Solve Traveling Salesman Problem Using Particle Swarm Optimization Algorithm,” International Journal of Computer Science Issues, vol. 9, no. 6-2, pp. 264–271, 2012. [10] K. P. Wang, L. Huang, C. G. Zhou and W. Pang, “Particle swarm optimization for traveling salesman problem,” in Proc. International Conference on Machine Learning and Cybernetics, November 2003, pp. 1583–1585. [11] X. Wei, Z. Jiang-wei and Z. Hon-lin, “Enhanced Self-Tentative Particle Swarm Optimization Algorithm for TSP,” Journal of north china electric power university, vol. 36, no. 6, pp. 69–74, 2009. [12] J. Zhang and W. Si, “Improved Enhanced Self-Tentative PSO Algorithm for TSP,” in Proc. Sixth IEEE International Conference on Natural Computation 2010, Yantai, Shandong, August 2010, pp. 2638–2641. [13] X. H. Shi, Y. C. Liang, H. P. Lee, C. Lu and Q. X. Wang, “Particle swarm optimization-based algorithms for TSP and generalized TSP,” Information Processing Letters, vol. 103, pp. 169–176, 2007. [14] H. Fan, “Discrete Particle Swarm Optimization for TSP based on Neighborhood,” Journal of Computational Information Systems (JCIS), vol. 6, pp. 3407–3414, 2010. [15] W. Zhong, J. Zhang and W. Chen, “A Novel Discrete Particle Swarm Optimization to solve Traveling Salesman problem,” IEEE Congress on Evolutionary Computation, 2007, pp. 3286–3287. [16] M. F. Tasgetiren, P. N. Suganthan and Q. Pan, “A Discrete Particle Swarm Optimization Algorithm for the Generalized Traveling Salesman Problem,” in Proc. 9th annual conference on Genetic And Evolutionary Computation, 2007, pp. 158–167. [17] E. F. G. Goldbarg, M. C. Goldbarg and G. R.de Souza, “Particle Swarm Optimization Algorithm for Traveling Salesman Problem,” Traveling Salesman Problem, Federico Greco(Ed.), InTech, 2008. [18] E. Montero, M. C. Riff and L. Altamirano, “A PSO algorithm to solve a Real Course+Exam Timetabling Problem,” International conference on swarm intelligence, Cergy, France, June 14–15, 2011, pp. 24-1–24-8. [19] R. Matai, S. Singh and M. L. Mittal, “Traveling Salesman Problem: an Overview of Applications, Formulations, and Solution Approaches,” Edited by D. Davendra, InTech, 2010, pp. 1–24. [20] TSPLIB - a library of sample instances for the TSP. Available: http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/ [21] E. Bonabeau, M. Dorigo and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, Oxford, 1999. [22] O. Cordon, F. Herrera, T. Stutzle, “A review on the ant colony optimization metaheuristic: basis, models and new trends,” Mathware and Soft Computing, vol. 9, pp. 141–175, 2002. [23] M. A. H. Akhand, P. C. Shill, Md. Forhad Hossen, A. B. M. Junaed and K. Murase, “Producer-Scrounger Method to Solve Traveling Salesman Problem,” I.J. Intelligent Systems and Applications (IJISA), vol. 7, no. 3, pp. 29–36, 2015. |
| originalfilename | 6565-01-FH02-FSTK-15-03714.jpg |
| person | UniSZA Unisza unisza |
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| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12265 |
| spelling | 12265 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12265 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal UniSZA Unisza unisza image/jpeg inches 96 96 11 11 1406 778 2015-09-03 09:21:09 1406x778 6565-01-FH02-FSTK-15-03714.jpg UniSZA Private Access Velocity tentative PSO: An optimal velocity implementation based particle swarm optimization to solve traveling salesman problem IAENG International Journal of Computer Science This paper introduces an effective Particle Swarm Optimization (PSO) based algorithm for solving Traveling Salesman Problem (TSP). Among prominent PSO based methods, the proposed Velocity Tentative PSO (VTPSO) considers Swap Sequence (SS) for velocity operation of the particles. A velocity SS is a collection of several Swap Operators (SOs) where each one indicates two positions in a tour those might be swapped. The existing methods apply all the SOs of the calculated SS on a solution to get a new solution. Conversely, the proposed VTPSO considers the calculated SS as the tentative velocity and checks the tentative solutions when applies the SOs one after another sequentially. The best tentative tour with a portion of SS is considered as the next solution point of a particle in VTPSO. Such intermediate tentative tour evaluation not only helps to get better solution but also reduces overall computational time. The experimental results on a large number of benchmark TSPs reveal that the proposed VTPSO is able to produce better tour compared to other prominent existing methods. 42 3 1-12 [1] R. Eberhart and J. Kennedy, “A New Optimizer Using Particles Swarm Theory,” in Proc. Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, October 1995, pp. 39–43. [2] J. J. Liang, A. K Qin, P. N Suganthan and S Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” Journal of evolutionary computation, vol. 10, no. 3, pp. 281–295, 2006. [3] A Banks, J. Vincent, and C. Anyakoha, “A review of particle swarm optimization. Part II: hybridization, combinatorial, multicriteria and constrained optimization, and indicative applications,” Journal of Natural Computing, vol. 7, no. 1, pp. 109–124, 2007. [4] L. Chuang, Y. Lin, and C. Yang, “Data Clustering Using Chaotic Particle Swarm Optimization,” IAENG International Journal of Computer Science, vol. 39, no. 2, pp. 208–213, 2012. [5] Z. Zhong and D. Pi, “Forecasting Satellite Attitude Volatility Using Support Vector Regression with Particle Swarm,” IAENG International Journal of Computer Science, vol. 41, no. 3, pp. 153– 162, 2014. [6] Hong Zhang, “An Analysis of Multiple Particle Swarm Optimizers with Inertia Weight for Multi-objective Optimization,” IAENG International Journal of Computer Science, vol. 39, no. 2, pp. 190– 199, 2012. [7] Y. F. Liao, D. H. Yau and C. L. Chen, “Evolutionary algorithm to traveling salesman problems,” Computers & Mathematics with Applications, Elsevier Publisher, vol. 64, no. 5, pp. 788–797, 2012. [8] M. R Bonyadi, M. R Azghadi and H. S Hosseini, “PopulationBased Optimization Algorithms for Solving the Travelling Salesman Problem,” Travelling Salesman Problem, Book edited by: Federico Greco, InTech Publisher, Vienna, Austria 2008. [9] X. Yan, C. Zhang, W. Luo, W Li, W, Chen and H. Liu, “Solve Traveling Salesman Problem Using Particle Swarm Optimization Algorithm,” International Journal of Computer Science Issues, vol. 9, no. 6-2, pp. 264–271, 2012. [10] K. P. Wang, L. Huang, C. G. Zhou and W. Pang, “Particle swarm optimization for traveling salesman problem,” in Proc. International Conference on Machine Learning and Cybernetics, November 2003, pp. 1583–1585. [11] X. Wei, Z. Jiang-wei and Z. Hon-lin, “Enhanced Self-Tentative Particle Swarm Optimization Algorithm for TSP,” Journal of north china electric power university, vol. 36, no. 6, pp. 69–74, 2009. [12] J. Zhang and W. Si, “Improved Enhanced Self-Tentative PSO Algorithm for TSP,” in Proc. Sixth IEEE International Conference on Natural Computation 2010, Yantai, Shandong, August 2010, pp. 2638–2641. [13] X. H. Shi, Y. C. Liang, H. P. Lee, C. Lu and Q. X. Wang, “Particle swarm optimization-based algorithms for TSP and generalized TSP,” Information Processing Letters, vol. 103, pp. 169–176, 2007. [14] H. Fan, “Discrete Particle Swarm Optimization for TSP based on Neighborhood,” Journal of Computational Information Systems (JCIS), vol. 6, pp. 3407–3414, 2010. [15] W. Zhong, J. Zhang and W. Chen, “A Novel Discrete Particle Swarm Optimization to solve Traveling Salesman problem,” IEEE Congress on Evolutionary Computation, 2007, pp. 3286–3287. [16] M. F. Tasgetiren, P. N. Suganthan and Q. Pan, “A Discrete Particle Swarm Optimization Algorithm for the Generalized Traveling Salesman Problem,” in Proc. 9th annual conference on Genetic And Evolutionary Computation, 2007, pp. 158–167. [17] E. F. G. Goldbarg, M. C. Goldbarg and G. R.de Souza, “Particle Swarm Optimization Algorithm for Traveling Salesman Problem,” Traveling Salesman Problem, Federico Greco(Ed.), InTech, 2008. [18] E. Montero, M. C. Riff and L. Altamirano, “A PSO algorithm to solve a Real Course+Exam Timetabling Problem,” International conference on swarm intelligence, Cergy, France, June 14–15, 2011, pp. 24-1–24-8. [19] R. Matai, S. Singh and M. L. Mittal, “Traveling Salesman Problem: an Overview of Applications, Formulations, and Solution Approaches,” Edited by D. Davendra, InTech, 2010, pp. 1–24. [20] TSPLIB - a library of sample instances for the TSP. Available: http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/ [21] E. Bonabeau, M. Dorigo and G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, Oxford, 1999. [22] O. Cordon, F. Herrera, T. Stutzle, “A review on the ant colony optimization metaheuristic: basis, models and new trends,” Mathware and Soft Computing, vol. 9, pp. 141–175, 2002. [23] M. A. H. Akhand, P. C. Shill, Md. Forhad Hossen, A. B. M. Junaed and K. Murase, “Producer-Scrounger Method to Solve Traveling Salesman Problem,” I.J. Intelligent Systems and Applications (IJISA), vol. 7, no. 3, pp. 29–36, 2015. |
| spellingShingle | Velocity tentative PSO: An optimal velocity implementation based particle swarm optimization to solve traveling salesman problem |
| summary | This paper introduces an effective Particle Swarm Optimization (PSO) based algorithm for solving Traveling Salesman Problem (TSP). Among prominent PSO based methods, the proposed Velocity Tentative PSO (VTPSO) considers Swap Sequence (SS) for velocity operation of the particles. A velocity SS is a collection of several Swap Operators (SOs) where each one indicates two positions in a tour those might be swapped. The existing methods apply all the SOs of the calculated SS on a solution to get a new solution. Conversely, the proposed VTPSO considers the calculated SS as the tentative velocity and checks the tentative solutions when applies the SOs one after another sequentially. The best tentative tour with a portion of SS is considered as the next solution point of a particle in VTPSO. Such intermediate tentative tour evaluation not only helps to get better solution but also reduces overall computational time. The experimental results on a large number of benchmark TSPs reveal that the proposed VTPSO is able to produce better tour compared to other prominent existing methods. |
| title | Velocity tentative PSO: An optimal velocity implementation based particle swarm optimization to solve traveling salesman problem |
| title_full | Velocity tentative PSO: An optimal velocity implementation based particle swarm optimization to solve traveling salesman problem |
| title_fullStr | Velocity tentative PSO: An optimal velocity implementation based particle swarm optimization to solve traveling salesman problem |
| title_full_unstemmed | Velocity tentative PSO: An optimal velocity implementation based particle swarm optimization to solve traveling salesman problem |
| title_short | Velocity tentative PSO: An optimal velocity implementation based particle swarm optimization to solve traveling salesman problem |
| title_sort | velocity tentative pso: an optimal velocity implementation based particle swarm optimization to solve traveling salesman problem |